Deep Learning Aided Low Complex Breadth-First Tree Search for MIMO Detection
Jieyu Liao, Junhui Zhao, Feifei Gao, Geoffrey Ye Li · IEEE Transactions on Wireless Communications · 2023
In this paper, we propose a deep learning based breadth-first sphere decoding (SD) scheme to reduce the detection complexity for multiple-input multiple-output (MIMO) communication systems. Specifically, we first design the DenseNet-based deep neural network (DN-DNN) to provide the pruning threshold for SD at each layer. Then, we develop modified number-based SD (MNSD) to reduce the complexity of SD by constraining the number of visited nodes at each layer with the output of DN-DNN. We use a distance-based SD (DSD) to further reduce the complexity of MNSD by constraining the accumulated distance at each layer with the output of DN-DNN. Compared with the traditional M-best SD withM= 16, the proposed MNSD achieves similar performance but reduces about 25% complexity for QPSK modulation; the proposed DSD has better performance with up to 75% complexity reduction at the high SNR region for 16QAM.